 Sentiment spin: Attacking financial sentiment with GPT-3Markus Leippold Finance Research Letters, 2023, vol. 55, issue PB Abstract: In this study, we explore the susceptibility of financial sentiment analysis to adversarial attacks that manipulate financial texts. With the rise of AI readership in the financial sector, companies are adapting their language and disclosures to fit AI processing better, leading to concerns about the potential for manipulation. In the finance literature, keyword-based methods, such as dictionaries, are still widely used for financial sentiment analysis due to their perceived transparency. However, our research demonstrates the vulnerability of keyword-based approaches by successfully generating adversarial attacks using the sophisticated transformer model, GPT-3. With a success rate of nearly 99% for negative sentences in the Financial Phrase Bank, a widely used database for financial sentiment analysis, we highlight the importance of incorporating robust methods, such as context-aware approaches such as BERT, in financial sentiment analysis. Keywords: sentiment analysis in financial markets; Keyword-based approach; FinBERT; GPT-3 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:55:y:2023:i:pb:s154461232300329x DOI: 10.1016/j.frl.2023.103957 Access Statistics for this article Finance Research Letters is currently edited by R. Gençay More articles in Finance Research Letters from Elsevier |
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